Day2_PM.Rmd

---
title: "Day 2 Project"
output: html_notebook
---

# Overview 

For this final project, we are going:  

* Setup a new git repo in R
* Sync it to a Github Repo
* Recreate an R Markdown document where we'll 
    + Explore the gapminder dataset
    + Create some data using loops and if else statements
    + Make some pretty plots to explore the data
* Commit changes and push to Github


# Specifics of R Notebook Exercise

Do the following inside the R Notebook:

1. Import necessary packages
    + Import `readr`, `dplyr`, and `ggplot2`
2. Read in data set
     + Use base R or `readr` to import gapminder
3. Explore `gapminder` dataset
     + Get summary statistics for `gapminder` variables
     + Get the structure of the `gapminder` dataset
     + Peek at the first 6 rows of `gapminder`
4. Manipulate `gapminder` to answer questions
     + Which country has the highest gdp per capita, and in what year?
     + What is the lowest life expectancy in the gapminder data set and what country had it?
     + ADVANCED: How many countries have ever had a life expectancy lower than 50
     + ADVANCED: How many total countries are there listed in Asia?
     + Make a new column that has the gdp (instead of `gdpPercap`) **Hint:** multiply `gdpPercap` by `pop` to get `gdp`.
5. Loops & if else
     + Create a loop that goes through each row and evaluates whether the population is above, below, or at the mean population.
          + Initialize a column, called `RelationToMeanPop` with `NA` values (syntax: `df$newColumnName <- NA`)
          + **for** each `row` of `1:nrow(gapminder2)`
          + **if** `gapminder2$pop[row] > mean(gapminder2$pop)`, assign `Above` to `gapminder2$RelationToMeanPop[row]` 
          + **else if** `gapminder2$pop[row] < mean(gapminder2$pop)`, assign `Below` to `gapminder2$RelationToMeanPop[row]`
          + **else if** `gapminder2$pop[row] == mean(gapminder2$pop)`, assign `At` to `gapminder2$RelationToMeanPop[row]`
          + **else** go to the next loop using `next`.
     + Run this for loop.  Don't forget to check your work by looking at the `RelationToMeanPop` column
     + Use `table(gapminder2$RelationToMeanPop)` to find out how many fit into each group.
6. Plots
     + Create a plot with `x=year`, `y=lifeExp`, and color by `continent`.  Use `geom_smooth()` to create a line to view life expectancy by year. Answer the following questions using the plot (you can verify by running some code).
          + What continent has the highest life expectancy?
          + What continent has the lowest life expectancy?
          + On what continents has the life expectancy been increasing or decreasing over time?
     + Create a plot with `x=continent`, `y=gdpPercap`, and fill by `continent`.  Use `geom_boxplot()` to create a boxplot for each continent. Use the plot to answer the following questions
          + Which continent tends to have the lowest gross domestic product (`gdp`)?
          + Which continent tends to have the highest gdp?
          + Which continent has the country with the highest gdp?
          
          
# Congratulations!!!

After completing this exercise, you're well on your way to becoming an **R Master**!  Remember, practice makes perfect.  Go back to look at some analysis you did in another program (like Excel) and see if you can figure out how to do it in R.

If you're ready to take the next steps, remember that we have a [resource list](../resources/CheatSheetsAndResources.Rmd) that can help you out.  Good luck on your R journey!  You've just taken some of the first steps, and there's a long road ahead, but we hope we've given you the essential tools to help you succeed!